ConvLSNet:基于ConvLSTM模型的轻量级架构,用于使用多通道肺声音记录来分类肺部疾病
Faezeh Majzoobi1, Mohammad Bagher Khodabakhshi1, Shahriar Jamasb1
1Biomedical Engineering Department, Hamedan University of Technology, Hamedan, Iran.
Artificial intelligence in medicine
|June 26, 2024
概括
一个新的深度学习模型,ConvLSNet,使用肺声音分析准确诊断肺部疾病,如喘和COPD. 这种先进的架构实现了高精度,降低了计算成本,改善了呼吸道诊断.
科学领域:
- 医疗信息学 医疗信息学
- 人工智能在医学中的应用
- 呼吸系统医学 呼吸系统医学
背景情况:
- 对肺部声音 (LS) 的准确表征对于诊断呼吸系统疾病至关重要.
- 传统的神经网络 (NN) 用于自动肺声诊断,但深度神经网络可以在不需要大量预处理的情况下提高准确性.
- 深度学习模型可以利用肺声时间序列中固有的顺序数据属性.
研究的目的:
- 开发一种新的深度神经网络架构,ConvLSNet,用于使用肺声音准确诊断肺病.
- 评估ConvLSNet在对喘,COPD和健康肺部声音记录进行分类方面的表现.
- 将ConvLSNet的准确性和计算效率与其他神经网络架构进行比较.
主要方法:
- 一个新的ConvLSNet架构被开发出来,连接了Convolutional Long Short-Term Memory (ConvLSTM) 和LSTM层.
- 该模型处理多通道肺声记录,同时捕捉空间和时间属性.
- ConvLSNet需要最小的预处理和数据转换来进行肺声分析.
主要成果:
- 拟议的ConvLSNet实现了97.4%的分类准确度,用于区分喘,COPD和健康的肺声状态.
- 与仅使用CNN或LSTM或2DCNN-LSTM级联的架构相比,ConvLSNet表现出更高的分类准确性.
- 在ConvLSNet架构中,计算成本最低,参数较少,培训时间缩短,学习率较低.
结论:
- 通过肺声分析,ConvLSNet架构提供了一种高度准确和计算高效的方法来诊断肺部疾病.
- 深度学习模型,特别是ConvLSNet,显示出对推进自动呼吸道诊断的重大前景.
- 在没有重重的预处理的情况下,ConvLSNet能够模拟肺声音的时空特性,这标志着一个显著的进步.
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